Abstract
The article describes the use of a self-learning neural network of the SOM type to forecast insolvency of enterprises in construction industry. The research was carried out on the basis of information regarding 578 enterprises that went into bankruptcy in the years 2007–2013. These entities constituted a sample singled out from a population of 4750 enterprises that went bankrupt in Poland during that time, for which it was possible to obtain financial statements in the form of balance sheets and profit-and-loss accounts for the period of 5 years prior to the bankruptcy. Twelve (12) variables in the form of financial analysis indicators have been assessed, which are most commonly used in the systems of early warning about insolvency. The network constructed allowed effective classification of nearly all entities as insolvent a year before the announcement of their bankruptcy.
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Notes
- 1.
International-wise, a synthetic description of the functionality of bankruptcy registers of EU member states, which in the future is to become a centralized database system, is available on the European Justice Web site: https://beta.e-justice.europa.eu/110/PL/bankruptcy_and_insolvency_registers; (accessed on: September 11, 2018).
- 2.
In accordance with the adopted research methodology, described in the first part of the study, the construction enterprises that went bankrupt in the years 2007–2013 have been described by financial analysis indicators and subjected to the indicator analysis. The indicators that required averaging of the balance sheet values, which directly results from the calculation procedure used in the financial analysis, should contain data for the reporting periods even 5 years prior to the year in which the bankruptcy took place. This often caused numerous data gaps, because despite the obligatory submission of such documents to the commercial courts, the entities subjected to restructuring transformations, particularly those at risk of bankruptcy, did not meet this obligation. The lack of sanctioning of the law in this aspect poses a big problem for the development of research on the models of early warning about bankruptcy, because it prevents acquisition of financial data regarding these entities, see e.g., [30].
- 3.
This risk was materialized in the form of a court ruling on the bankruptcy of these entities in the (t0) period.
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Migdał-Najman, K., Najman, K., Antonowicz, P. (2019). Early Warning Against Insolvency of Enterprises Based on a Self-learning Artificial Neural Network of the SOM Type. In: Tarczyński, W., Nermend, K. (eds) Effective Investments on Capital Markets. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-21274-2_12
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